Systematic Review on Various Techniques of Android Malware Detection

Smartphone has become the 4th basic necessity of human being after Food, Cloths and Home. It has become an integral part of the life that most of the business and office work can be operated by mobile phone and the demand for online classes demand for all class of students have become a compulsion without any alternate due to the COVID-19 pandemic. Android is considered as the most prevailing and used operating system for the mobile phone on this planet and for the same reason it is the most targeted mobile operating system by the hackers. Android malware has been increasing every quarter and every year. An android malware is installed and executed on the smartphones quietly without any indication and user’s acceptance, that possess threats to the consumer’s personal and/or classified information stored. To address these threats, varieties of techniques have been proposed by the researchers like Static, Dynamic and Hybrid. In this paper a systematic review has been carried out on the relevant studies from 2017 to 2020. Assessment of the malware detection capabilities of various techniques used by different researchers has been carried out with comparison of the performance of different machine learning models for the detection of android malwares by assessing the results of empirical evidences such as datasets, features, tools, etc. However the android malware detection still faces several challenges and the possible solution with some novel approach or technique to improve the detection capabilities is discussed in the discussion and conclusion. © 2022, Springer Nature Switzerland AG.

Detection Capability Prediction Based on Broad Learning System during the COVID-19 Pandemic

The greatest threat to global health is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) currently. COVID-19 was declared as a global pandemic on March 11, 2020. For this highly contagious disease, the way of human-to-human transmission has forced us to implement large-scale COVID-19 testing worldwide. On February 21, 2021, 120 million people have already undergone COVID-19 testing. The large scale of COVID-19 testing has driven innovation in strategies, technologies, and concepts for managing public health testing. It is an unprecedented global testing program. In this study, we describe the role of COVID-19 testing while establishing a comprehensive and validated research dataset that includes data from 189 countries and 893 regions between August 8, 2019, and March 3, 2021. Through our analysis, we observed that the more COVID-19 testings provided, the more confirmed cases were detected. The availability of large-scale COVID-19 testing is indispensable to fully control the outbreak, as it is the main way to cut off the source of COVID-19 transmission. Then we used this dataset to predict the COVID-19 detection capabilities of each country by Machine Learning, Ensemble Learning, and Broad Learning System. Experimental results show that Broad Learning System significantly outperformed the Machine Learning. The R2 of predicted the ability of the COVID-19 testing can reach 0.999921. © 2021 IEEE.

Optimization of an rGO-based biosensor for the sensitive detection of bovine serum albumin: Effect of electric field on detection capability.

Despite significant progress in the field of biosensing, the impact of electric field on biosensor detection capability and the feasibility of the biosensor application in wastewater has yet to be investigated. The objective of this study was to develop a low-cost, highly sensitive, and selective reduced graphene oxide (rGO)-based biosensor. The constructed biosensor consists of an in-house prepared GO and a four-terminal Kelvin sensing. Spin-coating was chosen as the deposition technique and results revealed an optimal GO number of layers and concentration of 7 and 2 mg/mL, respectively. Experiments to determine the effects of electric field on the performance of the biosensor showed significant changes in the biosensor surface, also presenting a direct impact on the biosensor functionality, such that the biosensor showed an increase in limit of detection (LOD) from 1 to 106 fg/mL when the applied voltage was increased from 0.0008 to 0.2 V. Furthermore, this study successfully explores a pilot scale setup, mimicking wastewater flow through sewage pipelines. The demonstrated improvements in the detection capability and sensitivity of this biosensor at optimized testing conditions make it a promising candidate for further development and deployment for the detection of protein analytes present at very low concentrations in aqueous solutions. In addition, the application of this biosensor could be extended to the detection of protein analytes of interest (such as the spike protein of SARS-CoV-2) in much more complex solutions, like wastewater.